{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c32023cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "C:\\python\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "# import pandas_datareader\n",
    "import datetime\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.pylab import style\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "style.use('ggplot')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a7fdd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     open  high   low  close  volume     amount\n",
      "candle_begin_time                                              \n",
      "2023-05-19 21:01:00  2189  2189  2172   2174   23550  512212500\n",
      "2023-05-19 21:02:00  2175  2180  2174   2176   12922  281418220\n",
      "2023-05-19 21:03:00  2175  2176  2172   2173    7513  163043030\n",
      "2023-05-19 21:04:00  2174  2176  2173   2175    4795  104291250\n",
      "2023-05-19 21:05:00  2175  2176  2174   2175    5098  110881500\n",
      "2023-05-19 21:06:00  2176  2176  2170   2170   11862  257998500\n",
      "2023-05-19 21:07:00  2171  2172  2169   2172    9744  211177160\n",
      "2023-05-19 21:08:00  2171  2172  2167   2167    9358  202594500\n",
      "2023-05-19 21:09:00  2168  2170  2166   2170    9728  211389440\n",
      "2023-05-19 21:10:00  2169  2170  2167   2168    6361  138224530\n"
     ]
    }
   ],
   "source": [
    "futures_df = pd.read_csv('futures/115.MA309.csv', index_col=0, parse_dates=[0])\n",
    "print(futures_df.head(n=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72c17c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candle_begin_time\n",
      "2023-05-19 21:01:00    2174\n",
      "2023-05-19 21:02:00    2176\n",
      "2023-05-19 21:03:00    2173\n",
      "2023-05-19 21:04:00    2175\n",
      "2023-05-19 21:05:00    2175\n",
      "2023-05-19 21:06:00    2170\n",
      "2023-05-19 21:07:00    2172\n",
      "2023-05-19 21:08:00    2167\n",
      "2023-05-19 21:09:00    2170\n",
      "2023-05-19 21:10:00    2168\n",
      "Name: close, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "futures_close = futures_df['close']\n",
    "# futures_train = futures_close['2023-05-20 09:01': '2023-05-20 23:00']\n",
    "futures_train = futures_close['2023-05-20 09:01': '2023-05-20 10:00']\n",
    "print(futures_close.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a948f02c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "futures_train.plot(figsize=(12, 8))\n",
    "plt.legend(bbox_to_anchor=(1.25, 0.5))\n",
    "plt.title('futures close')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55fbb7ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
